Application of soil parameter inversion method based on BP neural network in foundation pit deformation prediction

When significant deformation occurs in a foundation pit, it is critical to have an accurate method for predicting this deformation. This is necessary for enacting timely safety measures. Unfortunately, finite element simulations, which are strongly aff ected by soil parameters, fail to reflect the dynamic deformation of foundation pits during excavation. To address this, we used the actual soil parameters of a deep foundation pit to design 64 representative combinations of soil parameters through orthogonal testing. Using a three-dimensional (3D) finite element model of the foundation pit, we obtained displacement values for each parameter combination. These included the maximum horizontal displacement of the support structure and the surface settlement value. Subsequently, we developed a backpropagation (BP) neural network model. We trained this model using the soil parameters of each combination as input and the deformation values obtained from the 3D finite element model as output. Once the model was trained, we inverted the soil parameters, reflecting the dynamic deformation of the foundation pit by using actual monitoring data. This process allowed us to obtain the deformation data for the next excavation stage. Results showed that the soil parameters obtained via the BP neural network model eff ectively reflected the stress state of the deep foundation pit. Moreover, the prediction of the foundation pit deformation aligned well with the monitoring data, which validates the accuracy and feasibility of our method.